Point-in-time SEC XBRL fundamentals (80 filers, incl. delisted): every restatement vintage with its filed date. Pass as_of for what was knowable then. Filter by concept (e.g. Assets, Revenues).
| Name | Required | Description | Default |
|---|---|---|---|
| end | No | ||
| as_of | No | ||
| start | No | ||
| symbol | Yes | ||
| concept | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well by disclosing point-in-time semantics, inclusion of delisted filers, and that restatement vintages are returned with filed dates. It does not cover return structure or edge cases, but the core behavioral traits are clearly communicated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two tight sentences, front-loaded with the tool's purpose and key differentiators. Every phrase adds value without redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having no output schema and 5 parameters, the description covers the core functionality, scope, and two key parameters. It lacks explicit return-value details and start/end semantics, but for a read-oriented fundamentals tool, it is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It explicitly explains 'as_of' and 'concept', but leaves 'start', 'end', and 'symbol' undocumented. Symbol is obvious from the required field, but start/end date parameters are not explained, leaving a notable gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb+resource: it retrieves point-in-time SEC XBRL fundamentals. It further distinguishes the tool by mentioning the 80-filer universe, delisted companies, and restatement vintages with filed dates, which separates it from sibling tools focused on bars, events, and funding.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear usage context: 'Pass as_of for what was knowable then' and 'Filter by concept' provide specific how-to guidance. However, it does not explicitly discuss when not to use this tool or name alternative tools, so it falls shy of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools clearly target a distinct data resource: bars, events, fundamentals, funding, open interest, order flow, and so on. A few adjacent tools like audit_my_data and validate_backtest_data, or get_market_pulse and get_regime_label, are somewhat similar, but their descriptions provide enough separation for an agent to choose correctly.
The dominant pattern is get_<data_type>, used consistently across most tools and all in lowercase snake_case. The non-get tools are mostly still readable verb-noun names like build_bundle and validate_backtest_data, though lookahead_check and survivorship_check are minor deviations.
With 22 tools, this is on the heavier side for a single MCP server, especially since many tools have fairly specialized data sources. Each tool is individually justifiable, but the overall surface is large and may push agents to spend extra work choosing among near-adjacent data options.
The server covers far more than plain OHLCV: it includes fundamentals, insider and institutional ownership, funding rates, open interest, order flow, events, context, regime labels, and backtest-quality validation. Minor missing areas like trade-by-trade quotes or a broader symbol catalog mechanism exist, but the common market-data workflows are very well supported.